We test the gravitational-wave background (GWB) spectral consequences of the inter-containment framework against the corrected NANOGrav 15-year free-spectrum posteriors (v2.0.0, Zenodo 21844115; v1.x posteriors were biased by a parallel-tempering bug, erratum ApJL 1006 L67). Using the factorized ceffyl likelihood with...
wei Liu· Zenodo (CERN European Organi...· 0 citations
High-throughput inference for Large Language Models (LLMs) remains bottlenecked by memory bandwidth constraints and compute latency, particularly in hybrid edge-cloud topologies. This paper proposes an Adaptive Hybrid Quantization (AHQ) framework integrated with real-time speculative decoding. By dynamically allocating...
Bashar Mohammed Ali Ghaleb· Zenodo (CERN European Organi...· 0 citations
Physical Cybernetics: Restoring Substrate, Homeostasis, and Continuous Dynamics 70 Years After the Dartmouth Divergence Special 70th Anniversary Commemorative Treatise (1956–2026)Series: QN-PC-2026-V1.0 | Completed: August 2026 | Published: September 2026 Abstract Background: In the summer of 1956, the Dartmouth Summer...
Steven K· Zenodo (CERN European Organi...· 0 citations
The following material supports the article Agentic Mission Orchestration for Agricultural Robots: Coupling Agronomic Validity, Edge AI Reliability, and Data Governance, submitted to Precision Agriculture (Springer Nature). This deposit corresponds to the first revision (R1) of the manuscript and supersedes the materia...
Massimo Vecchio, Miguel Pincheira, Mattia Antonini et al.· Zenodo (CERN European Organi...· 0 citations
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The following material supports the article Agentic Mission Orchestration for Agricultural Robots: Coupling Agronomic Validity, Edge AI Reliability, and Data Governance, submitted to Precision Agriculture (Springer Nature). This deposit corresponds to the first revision (R1) of the manuscript and supersedes the materia...
Massimo Vecchio, Miguel Pincheira, Mattia Antonini et al.· Zenodo (CERN European Organi...· 0 citations
A safety-oriented architectural framework that constrains how AI-enabled functions may be integrated into fail-operational by-wire systems and clarifies trade-offs among safety isolation, latency, and AI performance while preserving braking safety guarantees under continuous software evolution is proposed.
Soumyasudharsan Srinivasaraghavan· SAE technical paper series· 0 citations
Near-infrared (NIR) imaging is essential for biological and materials sensing, yet high-performance NIR detectors are often cost-prohibitive and plagued by high dark current. Nonlinear frequency upconversion offers a compelling solution by shifting NIR signals into the visible spectrum, allowing the use of mature silic...
Meng-Zi Yin, Hong Wang, Mingwen Zhang et al.· Applied Physics Letters· 0 citations
This review systematically examines recent progress in AI-enabled energy management strategies for self-powered flexible electronic systems and categorizes AI integration across three key dimensions: at the algorithmic level, predictive models and reinforcement learning enable dynamic power allocation and coordinate mu...
Correction notice (v1.3). This version withdraws a claim. Through v1.2 the Discussion stated that this paper “completes part (ii)(a) for d=2” of the master conjecture of the Sakai stratification (10.5281/zenodo.20694840). Part (ii)(a) concerns the Edge–Borel coefficient, a different object from the connection coefficie...
Papanokechi· Zenodo (CERN European Organi...· 0 citations
The probabilistic cloud state machine (PCSM) theory [1] formalises the classical control layer of quantum computing as a geometric model over the space of stable probability distributions. This paper reports the full implementation path from that theory to a usable Python programming interface, together with real-hardw...
kaifa chen· Zenodo (CERN European Organi...· 0 citations
HoliBench is presented, a modular benchmarking and deployment toolkit that jointly characterizes accuracy, latency, and energy across platforms from single-board computers to GPU servers and is released as open-source infrastructure for deployment-aware evaluation of foundation models.
Inesh Chakrabarti, Ze-Jun Xiong, Pragya Sharma et al.· 0 citations
This work develops time and energy roofline characterizations for Orin AGX across power modes, coupled with analytical models of compute and memory for DNN inference, and extends these models to DNN training, and demonstrates power-mode tuning that achieves up to \(15\%\) lower energy with small inference-time impact f...
P. K, Kunal Kumar Sahoo, Amartya Ranjan Saikia et al.· Proceedings of the Internati...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
